from sklearn_benchmarks.report import Reporting, ReportingHpo, print_time_report, print_env_info
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
print_time_report()
daal4py_KMeans_short: 0h 0m 1s
daal4py_Ridge: 0h 0m 2s
KMeans_short: 0h 0m 3s
daal4py_LogisticRegression: 0h 0m 4s
daal4py_KMeans_tall: 0h 0m 9s
Ridge: 0h 0m 10s
LogisticRegression: 0h 0m 23s
KMeans_tall: 0h 0m 23s
daal4py_KNeighborsClassifier_kd_tree: 0h 0m 30s
KNeighborsClassifier_kd_tree: 0h 2m 33s
HistGradientBoostingClassifier: 0h 5m 3s
lightgbm: 0h 5m 3s
daal4py_KNeighborsClassifier: 0h 5m 8s
catboost_symmetric: 0h 5m 27s
catboost_lossguide: 0h 5m 39s
xgboost: 0h 5m 45s
KNeighborsClassifier: 0h 33m 4s
total: 1h 9m 35s
print_env_info()
{
"system_info": {
"python": "3.8.10 | packaged by conda-forge | (default, May 11 2021, 07:01:05) [GCC 9.3.0]",
"executable": "/usr/share/miniconda/envs/sklbench/bin/python",
"machine": "Linux-5.4.0-1047-azure-x86_64-with-glibc2.10"
},
"dependencies_info": {
"pip": "21.1.2",
"setuptools": "49.6.0.post20210108",
"sklearn": "1.0.dev0",
"numpy": "1.20.3",
"scipy": "1.6.3",
"Cython": null,
"pandas": "1.2.4",
"matplotlib": "3.4.2",
"joblib": "1.0.1",
"threadpoolctl": "2.1.0"
},
"threadpool_info": [
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
"prefix": "libopenblas",
"user_api": "blas",
"internal_api": "openblas",
"version": "0.3.15",
"num_threads": 2,
"threading_layer": "pthreads"
},
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
"prefix": "libgomp",
"user_api": "openmp",
"internal_api": "openmp",
"version": null,
"num_threads": 2
},
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
"prefix": "libgomp",
"user_api": "openmp",
"internal_api": "openmp",
"version": null,
"num_threads": 2
}
],
"cpu_count": 2
}
reporting = Reporting(config="config.yml")
reporting.run()
KNeighborsClassifier: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: algorithm=brute.
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.301 | 0.000 | 2.661 | 0.000 | -1 | 5 | NaN | NaN | 0.449 | 0.000 | 0.669 | 0.000 | See | See |
| 1 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 34.948 | 0.000 | 0.000 | 0.035 | -1 | 5 | 0.825 | 0.941 | 3.744 | 0.036 | 9.335 | 0.089 | See | See |
| 2 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.188 | 0.011 | 0.000 | 0.188 | -1 | 5 | 1.000 | 1.000 | 0.096 | 0.002 | 1.956 | 0.122 | See | See |
| 3 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.118 | 0.000 | 6.772 | 0.000 | 1 | 5 | NaN | NaN | 0.436 | 0.000 | 0.271 | 0.000 | See | See |
| 4 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 21.937 | 0.291 | 0.000 | 0.022 | 1 | 5 | 0.825 | 0.712 | 3.653 | 0.020 | 6.005 | 0.086 | See | See |
| 5 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.187 | 0.004 | 0.000 | 0.187 | 1 | 5 | 1.000 | 1.000 | 0.097 | 0.002 | 1.918 | 0.058 | See | See |
| 6 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.119 | 0.000 | 6.718 | 0.000 | -1 | 1 | NaN | NaN | 0.439 | 0.000 | 0.271 | 0.000 | See | See |
| 7 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 26.950 | 0.170 | 0.000 | 0.027 | -1 | 1 | 0.698 | 0.806 | 3.647 | 0.017 | 7.391 | 0.058 | See | See |
| 8 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.187 | 0.014 | 0.000 | 0.187 | -1 | 1 | 1.000 | 1.000 | 0.096 | 0.001 | 1.949 | 0.151 | See | See |
| 9 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.116 | 0.000 | 6.904 | 0.000 | -1 | 100 | NaN | NaN | 0.441 | 0.000 | 0.263 | 0.000 | See | See |
| 10 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 33.995 | 0.000 | 0.000 | 0.034 | -1 | 100 | 0.927 | 0.806 | 3.659 | 0.030 | 9.290 | 0.076 | See | See |
| 11 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.184 | 0.009 | 0.000 | 0.184 | -1 | 100 | 1.000 | 1.000 | 0.097 | 0.002 | 1.888 | 0.096 | See | See |
| 12 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.114 | 0.000 | 6.991 | 0.000 | 1 | 100 | NaN | NaN | 0.436 | 0.000 | 0.262 | 0.000 | See | See |
| 13 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 21.561 | 0.098 | 0.000 | 0.022 | 1 | 100 | 0.927 | 0.941 | 3.735 | 0.032 | 5.773 | 0.055 | See | See |
| 14 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.185 | 0.004 | 0.000 | 0.185 | 1 | 100 | 1.000 | 1.000 | 0.097 | 0.002 | 1.905 | 0.051 | See | See |
| 15 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.113 | 0.000 | 7.081 | 0.000 | 1 | 1 | NaN | NaN | 0.444 | 0.000 | 0.254 | 0.000 | See | See |
| 16 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 14.343 | 0.059 | 0.000 | 0.014 | 1 | 1 | 0.698 | 0.712 | 3.635 | 0.024 | 3.945 | 0.031 | See | See |
| 17 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.180 | 0.002 | 0.000 | 0.180 | 1 | 1 | 1.000 | 1.000 | 0.099 | 0.007 | 1.809 | 0.135 | See | See |
| 18 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.051 | 0.000 | 0.312 | 0.000 | -1 | 5 | NaN | NaN | 0.095 | 0.000 | 0.539 | 0.000 | See | See |
| 19 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 31.141 | 0.000 | 0.000 | 0.031 | -1 | 5 | 0.983 | 0.982 | 0.840 | 0.010 | 37.069 | 0.456 | See | See |
| 20 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.022 | 0.002 | 0.000 | 0.022 | -1 | 5 | 1.000 | 1.000 | 0.004 | 0.000 | 5.003 | 0.573 | See | See |
| 21 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.053 | 0.000 | 0.302 | 0.000 | 1 | 5 | NaN | NaN | 0.091 | 0.000 | 0.582 | 0.000 | See | See |
| 22 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 17.267 | 0.093 | 0.000 | 0.017 | 1 | 5 | 0.983 | 0.975 | 0.777 | 0.011 | 22.215 | 0.348 | See | See |
| 23 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.016 | 0.001 | 0.000 | 0.016 | 1 | 5 | 1.000 | 1.000 | 0.004 | 0.000 | 4.118 | 0.429 | See | See |
| 24 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.052 | 0.000 | 0.310 | 0.000 | -1 | 1 | NaN | NaN | 0.093 | 0.000 | 0.553 | 0.000 | See | See |
| 25 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 23.957 | 0.108 | 0.000 | 0.024 | -1 | 1 | 0.970 | 0.981 | 0.773 | 0.008 | 31.004 | 0.338 | See | See |
| 26 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.019 | 0.002 | 0.000 | 0.019 | -1 | 1 | 1.000 | 1.000 | 0.004 | 0.000 | 4.848 | 0.601 | See | See |
| 27 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.058 | 0.000 | 0.277 | 0.000 | -1 | 100 | NaN | NaN | 0.091 | 0.000 | 0.635 | 0.000 | See | See |
| 28 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 31.019 | 0.000 | 0.000 | 0.031 | -1 | 100 | 0.984 | 0.981 | 0.775 | 0.014 | 40.005 | 0.738 | See | See |
| 29 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.021 | 0.001 | 0.000 | 0.021 | -1 | 100 | 1.000 | 1.000 | 0.004 | 0.000 | 5.237 | 0.540 | See | See |
| 30 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.059 | 0.000 | 0.273 | 0.000 | 1 | 100 | NaN | NaN | 0.094 | 0.000 | 0.625 | 0.000 | See | See |
| 31 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 17.315 | 0.090 | 0.000 | 0.017 | 1 | 100 | 0.984 | 0.982 | 0.851 | 0.016 | 20.341 | 0.405 | See | See |
| 32 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.016 | 0.001 | 0.000 | 0.016 | 1 | 100 | 1.000 | 1.000 | 0.004 | 0.000 | 3.782 | 0.287 | See | See |
| 33 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.052 | 0.000 | 0.310 | 0.000 | 1 | 1 | NaN | NaN | 0.094 | 0.000 | 0.546 | 0.000 | See | See |
| 34 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 9.895 | 0.057 | 0.000 | 0.010 | 1 | 1 | 0.970 | 0.975 | 0.784 | 0.016 | 12.625 | 0.266 | See | See |
| 35 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.014 | 0.001 | 0.000 | 0.014 | 1 | 1 | 1.000 | 1.000 | 0.004 | 0.000 | 3.107 | 0.279 | See | See |
KNeighborsClassifier_kd_tree: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: algorithm=kd_tree.
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.849 | 0.000 | 0.028 | 0.000 | 1 | 100 | NaN | NaN | 0.709 | 0.000 | 4.020 | 0.000 | See | See |
| 1 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 4.698 | 0.022 | 0.000 | 0.005 | 1 | 100 | 0.975 | 0.962 | 0.109 | 0.001 | 43.245 | 0.496 | See | See |
| 2 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.003 | 0.001 | 0.000 | 0.003 | 1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 8.473 | 4.534 | See | See |
| 3 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.959 | 0.000 | 0.027 | 0.000 | 1 | 1 | NaN | NaN | 0.696 | 0.000 | 4.252 | 0.000 | See | See |
| 4 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.749 | 0.021 | 0.000 | 0.001 | 1 | 1 | 0.959 | 0.982 | 0.617 | 0.014 | 1.213 | 0.043 | See | See |
| 5 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 1.000 | 1.000 | 0.001 | 0.000 | 1.461 | 0.723 | See | See |
| 6 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.892 | 0.000 | 0.028 | 0.000 | -1 | 100 | NaN | NaN | 0.699 | 0.000 | 4.140 | 0.000 | See | See |
| 7 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 2.571 | 0.029 | 0.000 | 0.003 | -1 | 100 | 0.975 | 0.981 | 0.207 | 0.003 | 12.444 | 0.248 | See | See |
| 8 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.005 | 0.001 | 0.000 | 0.005 | -1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 13.685 | 5.917 | See | See |
| 9 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.818 | 0.000 | 0.028 | 0.000 | -1 | 5 | NaN | NaN | 0.708 | 0.000 | 3.979 | 0.000 | See | See |
| 10 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.786 | 0.017 | 0.000 | 0.001 | -1 | 5 | 0.978 | 0.982 | 0.620 | 0.010 | 1.268 | 0.035 | See | See |
| 11 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.003 | 0.000 | 0.000 | 0.003 | -1 | 5 | 1.000 | 1.000 | 0.001 | 0.000 | 4.365 | 1.602 | See | See |
| 12 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.826 | 0.000 | 0.028 | 0.000 | -1 | 1 | NaN | NaN | 0.727 | 0.000 | 3.887 | 0.000 | See | See |
| 13 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.416 | 0.011 | 0.000 | 0.000 | -1 | 1 | 0.959 | 0.962 | 0.114 | 0.002 | 3.645 | 0.121 | See | See |
| 14 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.003 | 0.001 | 0.000 | 0.003 | -1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 9.133 | 3.539 | See | See |
| 15 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.808 | 0.000 | 0.028 | 0.000 | 1 | 5 | NaN | NaN | 0.727 | 0.000 | 3.861 | 0.000 | See | See |
| 16 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 1.392 | 0.018 | 0.000 | 0.001 | 1 | 5 | 0.978 | 0.981 | 0.208 | 0.015 | 6.683 | 0.488 | See | See |
| 17 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 3.715 | 2.383 | See | See |
| 18 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.719 | 0.000 | 0.022 | 0.000 | 1 | 100 | NaN | NaN | 0.456 | 0.000 | 1.576 | 0.000 | See | See |
| 19 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.059 | 0.003 | 0.000 | 0.000 | 1 | 100 | 0.977 | 0.978 | 0.001 | 0.000 | 76.523 | 21.076 | See | See |
| 20 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 4.637 | 1.999 | See | See |
| 21 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.720 | 0.000 | 0.022 | 0.000 | 1 | 1 | NaN | NaN | 0.486 | 0.000 | 1.480 | 0.000 | See | See |
| 22 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.034 | 0.001 | 0.000 | 0.000 | 1 | 1 | 0.968 | 0.990 | 0.007 | 0.001 | 4.950 | 0.421 | See | See |
| 23 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 4.361 | 1.960 | See | See |
| 24 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.726 | 0.000 | 0.022 | 0.000 | -1 | 100 | NaN | NaN | 0.470 | 0.000 | 1.544 | 0.000 | See | See |
| 25 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.055 | 0.003 | 0.000 | 0.000 | -1 | 100 | 0.977 | 0.989 | 0.001 | 0.000 | 50.497 | 12.540 | See | See |
| 26 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.003 | 0.001 | 0.000 | 0.003 | -1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 14.778 | 7.226 | See | See |
| 27 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.743 | 0.000 | 0.022 | 0.000 | -1 | 5 | NaN | NaN | 0.467 | 0.000 | 1.591 | 0.000 | See | See |
| 28 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.039 | 0.003 | 0.000 | 0.000 | -1 | 5 | 0.976 | 0.990 | 0.007 | 0.001 | 5.747 | 0.680 | See | See |
| 29 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.003 | 0.001 | 0.000 | 0.003 | -1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 14.482 | 6.895 | See | See |
| 30 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.727 | 0.000 | 0.022 | 0.000 | -1 | 1 | NaN | NaN | 0.474 | 0.000 | 1.534 | 0.000 | See | See |
| 31 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.037 | 0.002 | 0.000 | 0.000 | -1 | 1 | 0.968 | 0.978 | 0.001 | 0.000 | 45.209 | 11.751 | See | See |
| 32 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.003 | 0.000 | 0.000 | 0.003 | -1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 14.831 | 7.357 | See | See |
| 33 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.723 | 0.000 | 0.022 | 0.000 | 1 | 5 | NaN | NaN | 0.485 | 0.000 | 1.489 | 0.000 | See | See |
| 34 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.037 | 0.002 | 0.000 | 0.000 | 1 | 5 | 0.976 | 0.989 | 0.001 | 0.000 | 34.065 | 8.462 | See | See |
| 35 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 6.245 | 3.548 | See | See |
KMeans_tall: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.
| estimator | function | n_samples_train | n_samples | n_features | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 30 | 0.677 | 0.000 | 0.709 | 0.000 | k-means++ | NaN | 30 | NaN | 0.285 | 0.0 | 2.375 | 0.000 | See | See |
| 1 | KMeans_tall | predict | 1000000 | 1000 | 2 | 30 | 0.001 | 0.000 | 0.321 | 0.000 | k-means++ | 0.001 | 30 | 0.001 | 0.000 | 0.0 | 6.507 | 2.199 | See | See |
| 2 | KMeans_tall | predict | 1000000 | 1 | 2 | 30 | 0.002 | 0.001 | 0.000 | 0.002 | k-means++ | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 7.034 | 3.352 | See | See |
| 3 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 30 | 0.498 | 0.000 | 0.964 | 0.000 | random | NaN | 30 | NaN | 0.303 | 0.0 | 1.645 | 0.000 | See | See |
| 4 | KMeans_tall | predict | 1000000 | 1000 | 2 | 30 | 0.002 | 0.000 | 0.298 | 0.000 | random | 0.001 | 30 | 0.000 | 0.000 | 0.0 | 6.659 | 2.832 | See | See |
| 5 | KMeans_tall | predict | 1000000 | 1 | 2 | 30 | 0.002 | 0.000 | 0.000 | 0.002 | random | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 5.279 | 1.586 | See | See |
| 6 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 30 | 7.073 | 0.000 | 3.393 | 0.000 | k-means++ | NaN | 30 | NaN | 3.494 | 0.0 | 2.025 | 0.000 | See | See |
| 7 | KMeans_tall | predict | 1000000 | 1000 | 100 | 30 | 0.002 | 0.001 | 11.164 | 0.000 | k-means++ | 0.002 | 30 | 0.001 | 0.000 | 0.0 | 6.824 | 3.593 | See | See |
| 8 | KMeans_tall | predict | 1000000 | 1 | 100 | 30 | 0.001 | 0.000 | 0.017 | 0.001 | k-means++ | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 7.517 | 3.345 | See | See |
| 9 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 30 | 6.295 | 0.000 | 3.812 | 0.000 | random | NaN | 30 | NaN | 3.661 | 0.0 | 1.719 | 0.000 | See | See |
| 10 | KMeans_tall | predict | 1000000 | 1000 | 100 | 30 | 0.002 | 0.000 | 13.300 | 0.000 | random | 0.002 | 30 | 0.002 | 0.000 | 0.0 | 4.822 | 2.007 | See | See |
| 11 | KMeans_tall | predict | 1000000 | 1 | 100 | 30 | 0.002 | 0.000 | 0.016 | 0.002 | random | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 6.737 | 2.921 | See | See |
KMeans_short: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.
| estimator | function | n_samples_train | n_samples | n_features | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_short | fit | 10000 | 10000 | 2 | 20 | 0.089 | 0.000 | 0.036 | 0.000 | random | NaN | 20 | NaN | 0.046 | 0.0 | 1.944 | 0.000 | See | See |
| 1 | KMeans_short | predict | 10000 | 1000 | 2 | 20 | 0.002 | 0.001 | 0.148 | 0.000 | random | -0.001 | 20 | 0.002 | 0.001 | 0.0 | 3.460 | 0.987 | See | See |
| 2 | KMeans_short | predict | 10000 | 1 | 2 | 20 | 0.002 | 0.000 | 0.000 | 0.002 | random | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 8.587 | 3.570 | See | See |
| 3 | KMeans_short | fit | 10000 | 10000 | 2 | 20 | 0.301 | 0.000 | 0.011 | 0.000 | k-means++ | NaN | 20 | NaN | 0.119 | 0.0 | 2.532 | 0.000 | See | See |
| 4 | KMeans_short | predict | 10000 | 1000 | 2 | 20 | 0.002 | 0.001 | 0.152 | 0.000 | k-means++ | -0.002 | 20 | 0.001 | 0.001 | 0.0 | 3.042 | 0.906 | See | See |
| 5 | KMeans_short | predict | 10000 | 1 | 2 | 20 | 0.002 | 0.000 | 0.000 | 0.002 | k-means++ | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 9.029 | 4.255 | See | See |
| 6 | KMeans_short | fit | 10000 | 10000 | 100 | 20 | 0.303 | 0.000 | 0.528 | 0.000 | random | NaN | 20 | NaN | 0.231 | 0.0 | 1.311 | 0.000 | See | See |
| 7 | KMeans_short | predict | 10000 | 1000 | 100 | 20 | 0.003 | 0.001 | 4.905 | 0.000 | random | 0.272 | 20 | 0.266 | 0.001 | 0.0 | 2.286 | 0.495 | See | See |
| 8 | KMeans_short | predict | 10000 | 1 | 100 | 20 | 0.002 | 0.000 | 0.009 | 0.002 | random | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 7.784 | 3.213 | See | See |
| 9 | KMeans_short | fit | 10000 | 10000 | 100 | 20 | 1.037 | 0.000 | 0.154 | 0.000 | k-means++ | NaN | 20 | NaN | 0.582 | 0.0 | 1.780 | 0.000 | See | See |
| 10 | KMeans_short | predict | 10000 | 1000 | 100 | 20 | 0.003 | 0.000 | 5.539 | 0.000 | k-means++ | 0.282 | 20 | 0.234 | 0.001 | 0.0 | 2.063 | 0.252 | See | See |
| 11 | KMeans_short | predict | 10000 | 1 | 100 | 20 | 0.002 | 0.000 | 0.010 | 0.002 | k-means++ | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 6.745 | 3.325 | See | See |
LogisticRegression: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | class_weight | l1_ratio | n_jobs | random_state | accuracy_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | LogisticRegression | fit | 1000000 | 1000000 | 100 | [20] | 14.567 | 0.000 | [-0.08099738] | 0.000 | NaN | NaN | NaN | NaN | NaN | 2.633 | 0.0 | 5.532 | 0.000 | See | See |
| 1 | LogisticRegression | predict | 1000000 | 1000 | 100 | [20] | 0.000 | 0.000 | [43.13082332] | 0.000 | NaN | NaN | NaN | NaN | 0.522 | 0.000 | 0.0 | 0.870 | 0.314 | See | See |
| 2 | LogisticRegression | predict | 1000000 | 1 | 100 | [20] | 0.000 | 0.000 | [0.14028762] | 0.000 | NaN | NaN | NaN | NaN | 1.000 | 0.000 | 0.0 | 0.383 | 0.253 | See | See |
| 3 | LogisticRegression | fit | 1000 | 1000 | 10000 | [27] | 1.195 | 0.000 | [-1.78615678] | 0.001 | NaN | NaN | NaN | NaN | NaN | 0.864 | 0.0 | 1.384 | 0.000 | See | See |
| 4 | LogisticRegression | predict | 1000 | 100 | 10000 | [27] | 0.002 | 0.001 | [89.37479723] | 0.000 | NaN | NaN | NaN | NaN | 0.280 | 0.004 | 0.0 | 0.598 | 0.148 | See | See |
| 5 | LogisticRegression | predict | 1000 | 1 | 10000 | [27] | 0.000 | 0.000 | [12.01989292] | 0.000 | NaN | NaN | NaN | NaN | 0.000 | 0.001 | 0.0 | 0.180 | 0.113 | See | See |
Ridge: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | max_iter | random_state | r2_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Ridge | fit | 1000 | 1000 | 10000 | NaN | 0.270 | 0.0 | 0.296 | 0.0 | NaN | NaN | NaN | 0.265 | 0.0 | 1.019 | 0.000 | See | See |
| 1 | Ridge | predict | 1000 | 1000 | 10000 | NaN | 0.010 | 0.0 | 7.802 | 0.0 | NaN | NaN | 0.09 | 0.017 | 0.0 | 0.614 | 0.020 | See | See |
| 2 | Ridge | predict | 1000 | 1 | 10000 | NaN | 0.000 | 0.0 | 0.742 | 0.0 | NaN | NaN | NaN | 0.000 | 0.0 | 0.697 | 0.420 | See | See |
| 3 | Ridge | fit | 1000000 | 1000000 | 100 | NaN | 1.383 | 0.0 | 0.579 | 0.0 | NaN | NaN | NaN | 0.345 | 0.0 | 4.008 | 0.000 | See | See |
| 4 | Ridge | predict | 1000000 | 1000 | 100 | NaN | 0.000 | 0.0 | 3.973 | 0.0 | NaN | NaN | 1.00 | 0.000 | 0.0 | 0.796 | 0.399 | See | See |
| 5 | Ridge | predict | 1000000 | 1 | 100 | NaN | 0.000 | 0.0 | 0.009 | 0.0 | NaN | NaN | NaN | 0.000 | 0.0 | 0.634 | 0.470 | See | See |